Automated extraction of significant clips from soccer video is known from an article by Assfalg et al, titled “Semantic annotation of soccer videos: automatic highlights identification” and published in Computer Vision and Image Understanding Vol. 92, pages 285-305.
Assfalg et al consider automatic detection of highlights based on the estimation of visual cues indications of state transitions in a finite state machine model of soccer game progress. One proposed visual cue is the localization of players. Assfalg et al use the distribution of the players' positions in specific regions of the playing field to detect placed kicks such as kick-offs. Since different types of placed kicks exhibit different players' deployments, the distribution of the players' positions in specific regions provides useful information in order to recognize the type of placed kick. Assfalg et al suggest to detect a cue for a kick-off by forming clusters of players' blobs based on their colors, and checking whether the clusters' bounding boxes are almost completely separated by the midfield line. However the use of blob clustering, which has to be performed for many images if no cues are to be missed, adds to the computational burden and time needed to detect events. This could prevent a real time response to soccer video.